GPT-4o vs Mistral Large (Bedrock): API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for GPT-4o (OpenAI) and Mistral Large (Bedrock) (AWS Bedrock). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.
Prices verified Mar 11, 2026
Interactive Cost Calculator
Fills in typical token counts for a workload type
Tokens in each prompt sent to the model
Tokens generated in each response
Total API calls per month
Showing costs for 2 models. Cheapest: GPT-4o at $75.00/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestGPT-4o | OpenAI | $75.00 | $0.007500 | $25.00 | $50.00 | 25.0% | Alerts coming soon |
Mistral Large (Bedrock) | AWS Bedrock | $100.00 | $0.010000 | $40.00 | $60.00 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | GPT-4o | Mistral Large (Bedrock) | Cheaper |
|---|---|---|---|
| Input (standard) | $2.50/M | $4.00/M | GPT-4o |
| Output | $10.00/M | $12.00/M | GPT-4o |
| Cached input | $1.25/M | N/A | — |
| Batch input | $1.25/M | N/A | — |
| Batch output | $5.00/M | N/A | — |
Prices last verified: 2026-03-06 – 2026-03-11
Cost Breakdown by Usage Volume
Estimated monthly costs at different request volumes, assuming 1,000 input tokens and 500 output tokens per request. Adjust in the calculator above for your specific use case.
| Volume | GPT-4oMonthly | Mistral Large (Bedrock)Monthly | GPT-4oPer request | Mistral Large (Bedrock)Per request |
|---|---|---|---|---|
| 1K requests/mo | $7.50 | $10.00 | $0.007500 | $0.010000 |
| 10K requests/mo | $75.00 | $100.00 | $0.007500 | $0.010000 |
| 100K requests/mo | $750.00 | $1,000.00 | $0.007500 | $0.010000 |
| 1M requests/mo | $7,500.00 | $10,000.00 | $0.007500 | $0.010000 |
Green values indicate the lower-cost option at each volume tier. Cost per request is calculated at 1,000 input + 500 output tokens using standard (non-batch, non-cached) pricing.
Price History
Input price per 1M tokensWhen to Choose GPT-4o
by OpenAI
- General chatbot
- Code generation
- Content & copywriting
Input / 1M tokens
$2.50/M
Output / 1M tokens
$10.00/M
Context window
128,000
Tier
premium
When to Choose Mistral Large (Bedrock)
by AWS Bedrock
- Code generation
- Content Creation
- Summarization
Input / 1M tokens
$4.00/M
Output / 1M tokens
$12.00/M
Context window
128,000
Tier
premium
Key Differences Beyond Price
Cost is only one factor in choosing an AI model. Context window size, rate limits, supported features, and latency all affect whether a model fits your use case.
| Capability | GPT-4o | Mistral Large (Bedrock) |
|---|---|---|
| Context window | 128,000 tokens | 128,000 tokens |
| Max output tokens | 16,384 tokens | 8,192 tokens |
| Performance tier | Premium | Premium |
| Vision / image input | Yes | No |
| Function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Prompt caching | Yes | No |
| Batch API (50% discount) | Yes | Yes |
| Extended reasoning | No | No |
| Fine-tuning | Yes | No |
| Rate limit (req/min) | 10,000 | Not published |
GPT-4o notes
Snapshot versions available (gpt-4o-2024-11-20). Cached input requires cache_control.
Mistral Large (Bedrock) notes
Mistral Large via AWS Bedrock. Strong multilingual performance. Useful for enterprises already on AWS that need Mistral's capabilities with native AWS security.
Frequently Asked Questions
Is GPT-4o cheaper than Mistral Large (Bedrock)?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), GPT-4o costs $750.00/month versus $1,000.00/month for Mistral Large (Bedrock) — a 25% saving. Your actual savings will vary based on your token profile; output-heavy workloads amplify differences in output pricing.
Which model has a larger context window, GPT-4o or Mistral Large (Bedrock)?
Both GPT-4o and Mistral Large (Bedrock) have the same context window: 128,000 tokens.
Do GPT-4o and Mistral Large (Bedrock) support the Batch API?
Yes — both GPT-4o and Mistral Large (Bedrock) support batch API processing, which offers a 50% discount on input and output costs in exchange for up to 24-hour turnaround. Ideal for offline workloads like bulk document processing or nightly classification pipelines.
Which model offers better prompt caching?
GPT-4o supports prompt caching at $1.25/M for cached input, while Mistral Large (Bedrock) does not offer prompt caching. For RAG applications or chatbots with large, repeated context, GPT-4o's caching capability can substantially reduce effective costs.
What are the best use cases for GPT-4o vs Mistral Large (Bedrock)?
Both models are well-suited for Code generation. GPT-4o is particularly strong for General chatbot, Content & copywriting. Mistral Large (Bedrock) is favored for Content Creation, Summarization. At the same quality level, the lower-cost model is usually preferable; use this page's calculator to compare total monthly spend at your volume.
What is the cost per request for GPT-4o vs Mistral Large (Bedrock)?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — GPT-4o costs $0.007500 per request and Mistral Large (Bedrock) costs $0.010000 per request. At 100,000 requests/month, that translates to $750.00 and $1,000.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
GPT-4o vs Mistral Large (Bedrock): Summary
When comparing GPT-4o and Mistral Large (Bedrock) for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. GPT-4o offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $750.00/month, compared to $1,000.00/month for Mistral Large (Bedrock).
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. GPT-4o charges $2.50/M for input tokens and $10.00/M for output tokens. Mistral Large (Bedrock) charges $4.00/M input and $12.00/M output. If your workload is output-heavy (more tokens generated than consumed as input), the model with the lower output price compounds cost savings significantly at scale.
Prompt caching is supported by GPT-4o but not Mistral Large (Bedrock). For workloads with large, repeated system prompts or document context — such as RAG pipelines or multi-turn conversations with a fixed knowledge base — prompt caching can reduce effective input costs by 60–90%, which may change the cost ranking between these two models at your specific usage pattern.
Context window capacity differs between the two: Both models support 128,000 tokens per request. A larger context window is essential for document summarization, large codebase analysis, and multi-document retrieval-augmented generation (RAG) applications.
Use the interactive calculator at the top of this page to enter your actual token usage and monthly request volume for a precise cost comparison tailored to your workload. Adjust for batch API discounts and prompt caching to find the most cost-effective option for your specific deployment.
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Relevant Use Cases
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